1. Field of the Invention
The present invention relates generally to imaging systems and more particularly to systems and methods for delineating anatomy from medical image data.
2. Description of Related Art
Medical treatment procedures based on medical image data often depend on morphological analyses of anatomic structures. Two-dimensional (2D) images from histology (immunohistochemistry) images and three-dimensional (3D) images from MRI, CT, PET, OCT, etc. imaging modalities provide computer accessible representations of a patient that can provide quantitative patient assessment. In the case of 3D MRI data, a radiologist examines the shape of a kidney, for example, as depicted in the image data to determine normality or the presence of a tumor. This depends upon the shape variation of the kidney observed in the patient image data using expert knowledge of how a normal kidney is shaped. Extrapolating that knowledge to how a particular patient's normal kidney should be shaped can aid in identifying anomalies indicating disease. Radiologists develop this expert knowledge throughout their career and often specialize in specific regions of the human body. Construction of a computer-based system replicating this knowledge and application thereof presents a complex set of tasks. The benefits of such a system would be substantial since that knowledge could be broadly applied in a more efficient manner. There is substantial research indicating that accurately obtained quantitative morphological measurements can be used for diagnosis, surgical indication, and/or severity quantification.
The main problem is obtaining an accurate anatomic representation to the extent that quantitative measurements can be taken reliably. This is generally referred to as image segmentation, which is the process of identifying the particular region encompassed by an anatomical part within image data. There are many applications of image segmentation, for example, textual characters can be identified on 2D images of typed documents [U.S. Pat. No. 6,298,151, U.S. Pat. No. 6,389,163, U.S. Pat. No. 6,157,736]; similar technology can be used to locate cells within immunohistochemistry images further allowing quantitative cellular analysis. In regard to 3D image segmentation, there are several methods that perform this task with varying success. These include threshold methods, atlas/template-based methods [U.S. Pat. No. 7,324,842, U.S. Pat. No. 5,926,568, 2006/0062425, 2007/0053589, 2007/0076932, 2007/0160277], active contour methods [U.S. Pat. No. 6,249,594], voxel-by-voxel region growing (flood fill) algorithms [U.S. Pat. No. 7,023,433, U.S. Pat. No. 5,185,809, U.S. Pat. No. 4,961,425, U.S. Pat. No. 5,319,551], pattern matching methods [2006/0056689], and machine vision methods [U.S. Pat. No. 7,346,209] to name a few. However, no general purpose segmentation method exists and most lack robustness across a variety of real-world patient data. The most common problem is the accurate delineation of the anatomic boundary which can be obscured by (i) poor image representation, (ii) disease, (iii) varying image intensity values throughout the anatomic part, (iv) similarities with other anatomical parts, and (v) touching anatomical parts. Furthermore, the boundary may simply be ill-defined such that general purpose image processing algorithms cannot accurately delineate anatomic boundaries.